Data Scientist Professional Practical Exam Submission¶

Use this template to write up your summary for submission. Code in Python or R needs to be included.

📝 Task List¶

Your written report should include both code, output and written text summaries of the following:

  • Data Validation:
    • Describe validation and cleaning steps for every column in the data
  • Exploratory Analysis:
    • Include two different graphics showing single variables only to demonstrate the characteristics of data
    • Include at least one graphic showing two or more variables to represent the relationship between features
    • Describe your findings
  • Model Development
    • Include your reasons for selecting the models you use as well as a statement of the problem type
    • Code to fit the baseline and comparison models
  • Model Evaluation
    • Describe the performance of the two models based on an appropriate metric
  • Business Metrics
    • Define a way to compare your model performance to the business
    • Describe how your models perform using this approach
  • Final summary including recommendations that the business should undertake

Start writing report here..

1 | Importing Libraries & Data ¶

In [33]:
# Data Manipulation and Analysis
import pandas as pd  # Data manipulation and analysis library

# Warnings and Errors
import warnings  # Library to ignore warnings in the code
warnings.filterwarnings('ignore')  # Ignore warnings in the code

# Data Visualization
import matplotlib.pyplot as plt  # Data visualization library
import seaborn as sns  # Statistical data visualization library

# Statistical Modeling and Computing
import statsmodels.api as sm  # Statistical modeling library
import scipy.stats as stats  # Scientific computing library for statistical functions
from scipy.stats import boxcox, skew  # Statistical functions for data transformation

# Machine Learning
from sklearn.tree import DecisionTreeClassifier  # Decision Tree Classifier for classification tasks
from sklearn.ensemble import RandomForestClassifier  # Random Forest Classifier for classification tasks
from sklearn.ensemble import IsolationForest  # Anomaly detection algorithm

# Data Preprocessing
from sklearn.preprocessing import MinMaxScaler  # Scaling/normalizing data
from sklearn.preprocessing import StandardScaler  # Scaling/normalizing data
from sklearn.preprocessing import LabelEncoder  # Encoding categorical variables
from sklearn.impute import SimpleImputer  # Handling missing values

# Model Selection and Evaluation
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score  # Model selection and evaluation
from sklearn.metrics import classification_report, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix  # Evaluation metrics for classification models


import flask   # Web Page Development
import joblib  # Library for saving and loading Python objects
In [3]:
df = pd.read_csv('recipe_site_traffic_2212.csv')
df.head()
Out[3]:
recipe calories carbohydrate sugar protein category servings high_traffic
0 1 NaN NaN NaN NaN Pork 6 High
1 2 35.48 38.56 0.66 0.92 Potato 4 High
2 3 914.28 42.68 3.09 2.88 Breakfast 1 NaN
3 4 97.03 30.56 38.63 0.02 Beverages 4 High
4 5 27.05 1.85 0.80 0.53 Beverages 4 NaN

2 | Understanding Our Data (EDA)¶

2.1 | Shape¶

In [4]:
#What is the shape of the dataset?
df.shape
Out[4]:
(947, 8)

2.2 | Statistical Summary of Dataset¶

In [24]:
# Generate summary statistics for numerical columns
summary_stats = df.describe(include='number')

# Function to style the DataFrame with a background color
def highlight_background(val):
    """Apply background color and text color to all cells."""
    return 'background-color: #eac086; color: black'

# Function to style the column headers
def apply_styles():
    """Apply consistent background color for headers."""
    styles = {
        'selector': 'th',
        'props': [('background-color', '#eac086'), ('color', 'black')]
    }
    return [styles]

# Apply the styling to the summary statistics DataFrame
styled_summary = summary_stats.style.applymap(highlight_background)\
                                     .set_table_styles(apply_styles())\
                                     .set_caption("Summary Statistics for Numerical Columns")

# Display the styled DataFrame
styled_summary
Out[24]:
Summary Statistics for Numerical Columns
  recipe calories carbohydrate sugar protein servings
count 574.000000 574.000000 574.000000 574.000000 574.000000 574.000000
mean 480.205575 451.673920 36.864286 7.893850 24.481098 3.545296
std 272.564979 456.225578 47.418157 12.645207 36.799484 1.744761
min 1.000000 0.140000 0.030000 0.010000 0.000000 1.000000
25% 245.250000 128.482500 9.645000 1.730000 4.952500 2.000000
50% 473.500000 288.550000 21.480000 4.550000 10.800000 4.000000
75% 719.750000 607.422500 45.292500 8.147500 28.625000 4.000000
max 946.000000 2906.010000 530.420000 131.390000 363.360000 6.000000

Summary Statistics Insights¶

Recipe Column¶

  • Mean: 480.21
  • Range: 1 to 946
  • Interquartile Range (IQR): 474.50

Calories¶

  • Mean: 451.67
  • Range: 0.14 to 2906.01
  • IQR: 478.94
  • Insight: This column shows a wide variability in calorie content, highlighting a broad range of recipe types from low-calorie to high-calorie.

Carbohydrate¶

  • Mean: 36.86
  • Range: 0.03 to 530.42
  • IQR: 35.64
  • Insight: Significant variation in carbohydrate content, reflecting a diverse array of recipes.

Sugar¶

  • Mean: 7.89
  • Range: 0.01 to 131.39
  • IQR: 6.42
  • Insight: Sugar content varies considerably, indicating recipes with both low and high sugar levels.

Protein¶

  • Mean: 24.48
  • Range: 0.00 to 363.36
  • IQR: 23.67
  • Insight: A broad range in protein content, suggesting recipes with varying protein levels from minimal to high.

Servings¶

  • Mean: 3.55
  • Range: 1 to 6
  • IQR: 2
  • Insight: Most recipes are designed to serve between 2 and 6 people, with a few exceptions.

2.3 | Information Dataset¶

In [4]:
# Extract information about DataFrame
df_info = pd.DataFrame({
    'Non-Null Count': df.notnull().sum(),
    'Data Type': df.dtypes
})

# Apply stylish formatting with custom colors
styled_df_info = (
    df_info.style
    .set_properties(**{
        'background-color': '#F0F8FF',  # Background color for the entire table
        'color': 'black',  # Text color
        'border': '1px solid black',  # Border color
        'padding': '8px'  # Padding for cells
    })
    .set_caption('DataFrame Information: Attributes and Data Types')  # Add a title to the table
    .set_table_styles([
        {'selector': 'th', 'props': [('background-color', '#F0F8FF')]},  # Bad heading background color
    ])
)

# Display the styled DataFrame
styled_df_info
Out[4]:
DataFrame Information: Attributes and Data Types
  Non-Null Count Data Type
recipe 947 int64
calories 895 float64
carbohydrate 895 float64
sugar 895 float64
protein 895 float64
category 947 object
servings 947 object
high_traffic 574 object

2.4 | Null Values Handling¶

In [6]:
# Calculate the number of null values and their percentages
null_counts = df.isnull().sum()
total_rows = len(df)
null_percentages = (null_counts / total_rows) * 100

# Create a DataFrame to display the counts and percentages
null_summary = pd.DataFrame({
    'Null Values': null_counts,
    'Percentage': null_percentages
})

# Apply stylish formatting with custom colors
styled_null_summary = (
    null_summary.style
    .format({'Percentage': '{:.2f}%'})  # Format percentage to two decimal places
    .background_gradient(cmap='coolwarm', subset=['Percentage'])  # Apply a gradient to the 'Percentage' column
    .highlight_max(subset=['Null Values'], color='lightcoral')  # Highlight the row with the maximum null values
    .set_caption('Summary of Null Values and Their Percentages')  # Add a title to the table
    .set_table_styles([
        {'selector': 'thead th', 'props': [('background-color', '#FFE4E1'),  # Header background color
                                           ('color', 'black'),  # Header text color
                                           ('font-weight', 'bold')]},
        {'selector': 'tbody tr:hover', 'props': [('background-color', '#FFE4E1')]},  # Hover effect with background color
        {'selector': 'tbody td', 'props': [('background-color', '#FFE4E1'),  # Table body background color
                                           ('color', 'black'),  # Table body text color
                                           ('border', '1px solid black'),  # Border color
                                           ('padding', '8px')]}
    ])
)

# Display the styled DataFrame
styled_null_summary
Out[6]:
Summary of Null Values and Their Percentages
  Null Values Percentage
recipe 0 0.00%
calories 52 5.49%
carbohydrate 52 5.49%
sugar 52 5.49%
protein 52 5.49%
category 0 0.00%
servings 0 0.00%
high_traffic 373 39.39%

Insights:¶

High Traffic Column:¶

This column has a significant amount of missing data (39.39%). This might indicate that the data is either not collected or is not applicable for a large portion of the dataset. Handling this column carefully is crucial to avoid skewing analysis results.

Nutritional Values:¶

Columns related to nutritional values (Calories, Carbohydrate, Sugar, Protein) each have 5.49% missing values. While not excessively high, this amount of missing data should be imputed to ensure robust analysis.

Complete Data Columns:¶

The Recipe, Category, and Servings columns have no missing values, ensuring that these attributes are fully accounted for in the dataset.

2.4.2 | Imputating Missing Values

In [6]:
# Define columns
numerical_columns = ['calories', 'carbohydrate', 'sugar', 'protein']
target_column = 'high_traffic'

# Step 1: Impute missing values in numerical columns with median
imputer = SimpleImputer(strategy='median')

# Impute missing values
df[numerical_columns] = imputer.fit_transform(df[numerical_columns])

# Step 2: Apply log transformation to skewed columns
df['calories_log'] = np.log1p(df['calories'])
df['carbohydrate_log'] = np.log1p(df['carbohydrate'])
df['sugar_log'] = np.log1p(df['sugar'])
df['protein_log'] = np.log1p(df['protein'])

# Impute missing values in transformed columns
df['calories_log'] = imputer.fit_transform(df[['calories_log']])
df['carbohydrate_log'] = imputer.fit_transform(df[['carbohydrate_log']])
df['sugar_log'] = imputer.fit_transform(df[['sugar_log']])
df['protein_log'] = imputer.fit_transform(df[['protein_log']])

# Reverse transformation to original scale
df['calories'] = np.expm1(df['calories_log'])
df['carbohydrate'] = np.expm1(df['carbohydrate_log'])
df['sugar'] = np.expm1(df['sugar_log'])
df['protein'] = np.expm1(df['protein_log'])

# Drop the log-transformed columns
df.drop(columns=['calories_log', 'carbohydrate_log', 'sugar_log', 'protein_log'], inplace=True)

# For target variable 'high_traffic'
# Option 1: Remove rows with missing 'high_traffic' values
df.dropna(subset=[target_column], inplace=True)
print("Null Values Have Been Handled")

Null Values Have Been Handled
In [7]:
# Calculate the number of null values and their percentages
null_counts = df.isnull().sum()
total_rows = len(df)
null_percentages = (null_counts / total_rows) * 100

# Create a DataFrame to display the counts and percentages
null_summary = pd.DataFrame({
    'Null Values': null_counts,
    'Percentage': null_percentages
})

# Apply stylish formatting with custom colors
styled_null_summary = (
    null_summary.style
    .format({'Percentage': '{:.2f}%'})  # Format percentage to two decimal places
    .background_gradient(cmap='coolwarm', subset=['Percentage'])  # Apply a gradient to the 'Percentage' column
    .highlight_max(subset=['Null Values'], color='lightcoral')  # Highlight the row with the maximum null values
    .set_caption('Summary of Null Values and Their Percentages')  # Add a title to the table
    .set_table_styles([
        {'selector': 'thead th', 'props': [('background-color', '#FFE4E1'),  # Header background color
                                           ('color', 'black'),  # Header text color
                                           ('font-weight', 'bold')]},
        {'selector': 'tbody tr:hover', 'props': [('background-color', '#FFE4E1')]},  # Hover effect with background color
        {'selector': 'tbody td', 'props': [('background-color', '#FFE4E1'),  # Table body background color
                                           ('color', 'black'),  # Table body text color
                                           ('border', '1px solid black'),  # Border color
                                           ('padding', '8px')]}
    ])
)

# Display the styled DataFrame
styled_null_summary
Out[7]:
Summary of Null Values and Their Percentages
  Null Values Percentage
recipe 0 0.00%
calories 0 0.00%
carbohydrate 0 0.00%
sugar 0 0.00%
protein 0 0.00%
category 0 0.00%
servings 0 0.00%
high_traffic 0 0.00%

2.5 | Duplicated Values Handling¶

In [11]:
# Calculate the number of duplicated rows
duplicate_count = df.duplicated().sum()
total_rows = len(df)
duplicate_percentage = (duplicate_count / total_rows) * 100

# Create a DataFrame to display the counts and percentages of duplicated rows
duplicate_summary = pd.DataFrame({
    'Duplicated Rows': [duplicate_count],
    'Percentage': [duplicate_percentage]
})

# Apply stylish formatting with custom colors
styled_duplicate_summary = (
    duplicate_summary.style
    .format({'Percentage': '{:.2f}%'})  # Format percentage to two decimal places
    .background_gradient(cmap='coolwarm', subset=['Percentage'])  # Apply a gradient to the 'Percentage' column
    .highlight_max(subset=['Duplicated Rows'], color='lightcoral')  # Highlight the row with the maximum duplicated rows
    .set_caption('Summary of Duplicated Values and Their Percentages')  # Add a title to the table
    .set_table_styles([
        {'selector': 'thead th', 'props': [('background-color', '#FFE4E1'),  # Header background color
                                           ('color', 'black'),  # Header text color
                                           ('font-weight', 'bold')]},
        {'selector': 'tbody tr:hover', 'props': [('background-color', '#FFE4E1')]},  # Hover effect with background color
        {'selector': 'tbody td', 'props': [('background-color', '#FFE4E1'),  # Table body background color
                                           ('color', 'black'),  # Table body text color
                                           ('border', '1px solid black'),  # Border color
                                           ('padding', '8px')]}
    ])
)

# Display the styled DataFrame
styled_duplicate_summary
Out[11]:
Summary of Duplicated Values and Their Percentages
  Duplicated Rows Percentage
0 0 0.00%

2.6 | Visualizations¶

2.6.1 | Numeric Features Visualization¶

In [12]:
# Customize the color palette
colors = ['#eac086', '#ffcd94', '#e57373']

# Create subplots
fig, ax = plt.subplots(6, 3, figsize=(50, 70))

for index, column in enumerate(df.select_dtypes(include='number').columns):
    # Distribution Plot with KDE
    sns.histplot(df[column], kde=True, color=colors[0], alpha=0.9, ax=ax[index, 0], bins=30, edgecolor='black')
    ax[index, 0].set_title(f'Distribution Plot of {column}', fontsize=14, weight='bold')
    ax[index, 0].set_xlabel(column, fontsize=12)
    ax[index, 0].set_ylabel('Frequency', fontsize=12)
    ax[index, 0].grid(True)

    # Boxplot
    sns.boxplot(x=df[column], ax=ax[index, 1], color=colors[1], saturation=0.9)
    ax[index, 1].set_title(f'Box Plot of {column}', fontsize=14, weight='bold')
    ax[index, 1].set_xlabel(column, fontsize=12)
    ax[index, 1].grid(True)

    # Q-Q Plot for Normality Check
    stats.probplot(df[column].dropna(), plot=ax[index, 2])
    ax[index, 2].get_lines()[1].set_color(colors[2])  # Line of the Q-Q plot
    ax[index, 2].get_lines()[1].set_linewidth(2)
    ax[index, 2].set_title(f'Q-Q Plot of {column}', fontsize=14, weight='bold')
    ax[index, 2].grid(True)

# Improve overall layout and add a main title
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.subplots_adjust(top=0.95, hspace=0.4)
plt.suptitle("Visualizing Continuous Columns", fontsize=50, weight='bold', color='black')

plt.show()
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2.6.2 | Categorical Features Visualization¶

In [11]:
# Create subplots for categorical columns
fig, ax = plt.subplots(len(df.select_dtypes(include='object').columns), 1, figsize=(10, 20))

for index, column in enumerate(df.select_dtypes(include='object').columns):
    # Count Plot for categorical columns
    sns.countplot(x=column, data=df, ax=ax[index], palette=[colors[0], colors[1]])
    ax[index].set_title(f'Count Plot of {column}', fontsize=14, weight='bold')
    ax[index].set_xlabel(column, fontsize=12)
    ax[index].set_ylabel('Count', fontsize=12)
    ax[index].grid(True)

# Improve overall layout and add a main title
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.subplots_adjust(top=0.95, hspace=0.4)
plt.suptitle("Visualizing Categorical Columns", fontsize=20, weight='bold', color='black')

plt.show()
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2.6.3 | Relational Visualizations

  1. Correlation Matrix
  2. Scatter Plot
In [27]:
# Define color palette
colors = ['#eac086', '#FFE4E1', '#F0F8FF']

# Set the background color for all plots
plt.style.use('dark_background')

# Dropping non-numeric columns for correlation
numeric_df = df[['calories', 'carbohydrate', 'sugar', 'protein', 'servings']]

# 1. Correlation Heatmap
plt.figure(figsize=(12, 8))
heatmap = sns.heatmap(numeric_df.corr(), annot=True, cmap=colors, fmt='.2f', 
                      linewidths=0.5, linecolor='black', cbar_kws={'shrink': 0.8})
plt.title('Correlation Heatmap of Nutritional Variables', fontsize=16, weight='bold', color='white')
plt.xticks(ticks=range(len(numeric_df.columns)), labels=numeric_df.columns, rotation=45, fontsize=12, color='white')
plt.yticks(ticks=range(len(numeric_df.columns)), labels=numeric_df.columns, fontsize=12, color='white')
plt.show()

# 2. Scatter Plot: Calories vs. Protein by Category
plt.figure(figsize=(12, 8))
scatter_plot = sns.scatterplot(data=df, x='calories', y='protein', hue='category', palette=colors, s=100, edgecolor='black')
plt.title('Scatter Plot of Calories vs. Protein by Recipe Category', fontsize=16, weight='bold', color='white')
plt.xlabel('Calories', fontsize=14, weight='bold', color='white')
plt.ylabel('Protein', fontsize=14, weight='bold', color='white')
plt.xticks(fontsize=12, color='white')
plt.yticks(fontsize=12, color='white')
plt.legend(title='Category', title_fontsize='13', fontsize='11', loc='upper right', frameon=True, edgecolor='black')
plt.grid(True, linestyle='--', alpha=0.7)
plt.show()
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2.6.4 | More Relational Visualizations

  1. Pair Plots
  2. Box Plots
  3. Count Plots
In [19]:
# Define color palette
colors = ['#eac086', '#FFE4E1', '#F0F8FF']

# 1. Pair Plot
plt.figure(figsize=(12, 8))
pair_plot = sns.pairplot(df[['calories', 'carbohydrate', 'sugar', 'protein', 'servings']], 
                        kind='scatter', 
                        diag_kind='hist', 
                        palette=colors)
plt.suptitle('Pair Plot of Nutritional Variables', y=1.02, fontsize=16, weight='bold', color='white')
plt.subplots_adjust(top=0.95) 
plt.show()

# 2. Box Plot: Distribution of Calories by Category
plt.figure(figsize=(12, 6))
box_plot = sns.boxplot(data=df, x='category', y='calories', palette=colors)
plt.title('Box Plot of Calories Distribution by Recipe Category', fontsize=16, weight='bold', color='white')
plt.xlabel('Recipe Category', fontsize=14, weight='bold', color='white')
plt.ylabel('Calories', fontsize=14, weight='bold', color='white')
plt.xticks(rotation=45, fontsize=12, color='white')
plt.yticks(fontsize=12, color='white')
plt.grid(True, linestyle='--', alpha=0.7, color='white')
plt.show()

# 3. Count Plot: Distribution of Recipe Categories
plt.figure(figsize=(10, 5))
count_plot = sns.countplot(data=df, x='category', palette=colors)
plt.title('Count Plot of Recipe Categories Distribution', fontsize=16, weight='bold', color='white')
plt.xlabel('Recipe Category', fontsize=14, weight='bold', color='white')
plt.ylabel('Count', fontsize=14, weight='bold', color='white')
plt.xticks(rotation=45, fontsize=12, color='white')
plt.yticks(fontsize=12, color='white')
plt.grid(True, linestyle='--', alpha=0.7, color='white')
plt.show()

<Figure size 1200x800 with 0 Axes>
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2.7 | Skewness Handling

In [17]:
# Step 1: Calculate Skewness Before Transformation
# Select only numeric columns for skewness calculation
numeric_columns = df.select_dtypes(include='number').columns
skewness_before = df[numeric_columns].skew(axis=0).sort_values()
skewness_df_before = pd.DataFrame(skewness_before, columns=['Skewness Before'])

# Step 2: Apply Transformations and Store the Transformed Features
transformed_features = {}
skewness_after = []

for col in numeric_columns:
    # Check if column values are all positive (required for Box-Cox transformation)
    if (df[col] > 0).all():
        transformed_data, fitted_lambda = boxcox(df[col].dropna())
        transformed_features[col] = transformed_data  # Store transformed data
        skewness_after.append({'Column': col, 'Skewness After': skew(transformed_data)})
    else:
        # Apply log transformation for non-positive values (handle zeros by adding a small constant)
        transformed_data = np.log1p(df[col] - df[col].min() + 1)
        transformed_features[col] = transformed_data
        skewness_after.append({'Column': col, 'Skewness After': skew(transformed_data)})

# Convert skewness after transformation to DataFrame
skewness_df_after = pd.DataFrame(skewness_after).set_index('Column')

# Step 3: Use Pandas Styling to Display Skewness Tables Before and After Transformation
styled_skewness_before = (
    skewness_df_before.style
    .background_gradient(cmap='coolwarm')
    .set_properties(**{
        'background-color': '#FFE4E1',  # Set custom background color
        'color': 'black',  # Set text color to black
        'border': '1px solid black',  # Border color
        'padding': '8px'  # Padding for better readability
    })
    .set_caption('------- Column Skewness Before Transformation ------')
)

styled_skewness_after = (
    skewness_df_after.style
    .background_gradient(cmap='coolwarm')
    .set_properties(**{
        'background-color': '#F0F8FF',  # Set custom background color
        'color': 'black',  # Set text color to black
        'border': '1px solid black',  # Border color
        'padding': '8px'  # Padding for better readability
    })
    .set_caption('---- Column Skewness After Transformation ---')
)

# Display the styled DataFrames
display(styled_skewness_before)
display(styled_skewness_after)
------- Column Skewness Before Transformation ------
  Skewness Before
servings -0.051301
recipe -0.004502
calories 1.875225
protein 3.778232
carbohydrate 4.054543
sugar 4.409599
---- Column Skewness After Transformation ---
  Skewness After
Column  
recipe -0.280374
calories -0.025410
carbohydrate 0.025765
sugar 0.024352
protein 0.299524
servings -0.209956

2.8 | Outliers Handling

In [28]:
# Define numerical columns for analysis
numerical_columns = ['calories', 'carbohydrate', 'sugar', 'protein']

# 🔍 **Feature Scaling** - Standardization using MinMaxScaler
scaler = MinMaxScaler()
df_scaled = df.copy()
df_scaled[numerical_columns] = scaler.fit_transform(df[numerical_columns])

# 🌲 **Initialize the Isolation Forest Model** - Detect anomalies with advanced settings
iso_forest = IsolationForest(
    n_estimators=100,        # Number of base estimators
    max_samples='auto',      # Number of samples to draw from X to train each base estimator
    contamination='auto',    # Proportion of outliers in the data set
    max_features=1.0,        # Number of features to draw from X to train each base estimator
    bootstrap=False,         # Whether samples are drawn with replacement
    n_jobs=-1,               # Number of parallel jobs
    random_state=42          # Seed for random number generator
)

# Fit the model
df_scaled['outlier'] = iso_forest.fit_predict(df_scaled[numerical_columns])

# -1 indicates an outlier, 1 indicates a normal point
outliers = df_scaled[df_scaled['outlier'] == -1]
print(f"🌟 Number of outliers detected: {len(outliers)}")

#  **Stylize DataFrame** - Highlight outliers with background color #eac086
def highlight_outliers(row):
    color = '#eac086' if row['outlier'] == -1 else ''
    return ['background-color: {}'.format(color) for _ in row]

# Apply styling to DataFrame
styled_df = df_scaled.style.apply(highlight_outliers, axis=1)

# Display the styled DataFrame
styled_df

🌟 Number of outliers detected: 78
Out[28]:
  recipe calories carbohydrate sugar protein category servings high_traffic outlier
0 1 0.099251 0.040442 0.034556 0.029723 Pork 6 High 1
1 2 0.012162 0.072645 0.004947 0.002532 Potato 4 High 1
3 4 0.033343 0.057561 0.293956 0.000055 Beverages 4 High 1
5 6 0.237798 0.006467 0.012483 0.148420 One Dish Meal 2 High 1
8 9 0.185273 0.007070 0.025575 0.010430 Pork 6 High 1
12 13 0.094461 0.044232 0.011798 0.007073 Potato 4 High 1
13 14 0.008634 0.021644 0.078475 0.026338 Vegetable 4 High 1
14 15 0.074676 0.012557 0.076039 0.041749 Meat 4 High 1
15 16 0.108852 0.004940 0.035546 0.219369 Meat 6 High 1
16 17 0.156280 0.003469 0.022378 0.168070 Meat 2 High 1
17 18 0.583536 0.000132 0.002892 0.091287 Meat 1 High -1
18 19 0.375313 0.008711 0.005176 0.009605 Meat 6 High 1
20 21 0.003138 0.032825 0.062034 0.029750 Potato 6 High 1
24 25 0.399657 0.002828 0.067514 0.034594 Breakfast 1 High 1
25 26 0.019323 0.042082 0.086543 0.095745 One Dish Meal 4 High 1
26 27 0.141445 0.097419 0.211372 0.193472 Pork 2 High -1
27 28 0.197741 0.024680 0.013929 0.038116 Potato 4 High 1
28 29 0.204844 0.118102 0.020018 0.013650 Potato 2 High 1
29 30 0.056651 0.063255 0.135942 0.605845 One Dish Meal 2 High -1
30 31 0.074277 0.099229 0.047496 0.088948 Pork 2 High 1
33 34 0.171043 0.002715 0.011417 0.008174 Lunch/Snacks 6 High 1
34 35 0.198044 0.038990 0.001446 0.017173 Breakfast 6 High 1
39 40 0.015372 0.008654 0.002968 0.016237 Vegetable 4 High 1
40 41 0.213843 0.026641 0.081367 0.109231 Chicken Breast 6 High 1
45 46 0.028649 0.024567 0.012255 0.009467 Vegetable 6 High 1
49 50 0.593320 0.085767 0.000457 0.135871 Breakfast 1 High -1
51 52 0.440278 0.036822 0.008144 0.050088 Pork 4 High 1
53 54 0.025610 0.089444 0.124448 0.124257 Pork 4 High 1
54 55 0.045208 0.062765 0.012863 0.239735 Pork 6 High 1
55 56 0.730280 0.048945 0.003882 0.223938 Pork 1 High -1
56 57 0.016184 0.114821 0.105115 0.029723 Vegetable 4 High 1
58 59 0.002096 0.106337 0.042548 0.005834 Lunch/Snacks 6 High 1
60 61 0.212263 0.130885 0.059065 0.008587 Dessert 4 High 1
62 63 0.055030 0.079979 0.012102 0.122138 One Dish Meal 2 High 1
63 64 0.279758 0.166896 0.003501 0.376734 Chicken 1 High -1
64 65 0.194799 0.003186 0.019257 0.011669 Meat 4 High 1
74 75 0.110270 0.157922 0.123535 0.005697 Dessert 1 High 1
75 76 0.263955 0.046626 0.004262 0.085507 Potato 4 High 1
76 77 0.191860 0.069289 0.022758 0.053803 One Dish Meal 1 High 1
79 80 0.006803 0.066819 0.016441 0.032530 Breakfast 1 High 1
80 81 0.028019 0.062313 0.017735 0.030823 Breakfast 4 High 1
81 82 0.022558 0.015008 0.070178 0.039960 Breakfast 4 High 1
82 83 0.099251 0.040442 0.034556 0.029723 Meat 4 High 1
84 85 0.029261 0.056430 0.004262 0.014421 Chicken 2 High 1
85 86 0.228035 0.089708 0.030979 0.006577 Meat 1 High 1
86 87 0.038849 0.044156 0.003501 0.170850 Meat 4 High 1
88 89 0.066428 0.008409 0.022835 0.129073 Breakfast 4 High 1
89 90 0.099251 0.040442 0.034556 0.029723 Pork 6 High 1
91 92 0.036729 0.001603 0.055336 0.015604 Vegetable 4 High 1
94 95 0.284490 0.273931 0.049246 0.008366 Potato 4 High 1
96 97 0.030432 0.026924 0.360100 0.015604 Dessert 1 High 1
97 98 0.716326 0.015196 0.036307 0.078380 One Dish Meal 2 High -1
98 99 0.045308 0.054960 0.044451 0.023173 Breakfast 2 High 1
101 102 0.068427 0.165256 0.002892 0.107662 Vegetable 4 High 1
103 104 0.370206 0.006863 0.114858 0.125550 Meat 4 High 1
105 106 0.136355 0.031713 0.018800 0.000743 Beverages 1 High 1
107 108 0.067636 0.004242 0.069341 0.005972 Vegetable 1 High 1
108 109 0.210780 0.072626 0.076496 0.015549 Dessert 6 High 1
109 110 0.055467 0.060672 0.054346 0.016760 Lunch/Snacks 4 High 1
111 112 0.140929 0.058994 0.105191 0.082177 Meat 1 High 1
112 113 0.247389 0.093026 0.009210 0.015742 Potato 4 High 1
113 114 0.026371 0.092027 0.174608 0.013430 Meat 2 High 1
114 115 0.061152 0.020061 0.016745 0.008311 Lunch/Snacks 2 High 1
115 116 0.009161 0.009691 0.018115 0.012247 Vegetable 4 High 1
116 117 0.099251 0.040442 0.034556 0.029723 Chicken Breast 6 High 1
119 120 0.152471 0.030487 0.078703 0.000028 Vegetable 4 High 1
120 121 0.383637 0.059239 0.099026 0.003660 Meat 2 High 1
121 122 0.099251 0.040442 0.034556 0.029723 Dessert 2 High 1
124 125 1.000000 0.006580 0.014310 0.493230 Pork 6 High -1
125 126 0.062040 0.018119 0.052215 0.042905 Breakfast 4 High 1
126 127 0.111450 0.149230 0.012863 0.019347 One Dish Meal 4 High 1
127 128 0.330576 0.359754 0.031359 0.266705 One Dish Meal 4 High -1
131 132 0.055040 0.026735 0.077714 0.010485 Potato 4 High 1
132 133 0.032169 0.006995 0.223778 0.012715 Dessert 4 High 1
134 135 0.035827 0.084051 0.038895 0.007954 Vegetable 4 High 1
135 136 0.039854 0.294500 0.060055 0.136586 One Dish Meal 4 High -1
136 137 0.099251 0.040442 0.034556 0.029723 One Dish Meal 2 High 1
137 138 0.008228 0.112370 0.002283 0.390880 Chicken Breast 4 High -1
138 139 0.033009 0.097871 0.020094 0.035970 Breakfast 6 High 1
139 140 0.004639 0.173438 0.006698 0.123624 One Dish Meal 1 High 1
142 143 0.032441 0.048926 0.181002 0.004101 Dessert 4 High 1
144 145 0.102131 0.002621 0.084792 0.069325 Breakfast 2 High 1
146 147 0.078957 0.075228 0.008220 0.018329 Potato 4 High 1
148 149 0.070836 0.116556 0.021921 0.039768 Chicken 4 High 1
149 150 0.099251 0.040442 0.034556 0.029723 Potato 2 High 1
150 151 0.098559 0.039725 0.011569 0.011284 Breakfast 6 High 1
151 152 0.107111 0.081393 0.008525 0.009247 Vegetable 1 High 1
152 153 0.061544 0.008126 0.131527 0.050886 Vegetable 4 High 1
154 155 0.064793 0.064594 0.042168 0.008669 Breakfast 4 High 1
158 159 0.115384 0.044967 0.021008 0.105075 Vegetable 6 High 1
159 160 0.267469 0.191746 0.019105 0.014091 Dessert 4 High 1
160 161 0.121630 0.024114 0.007688 0.017586 One Dish Meal 1 High 1
161 162 0.295598 0.137880 0.111052 0.009082 Dessert 6 High 1
163 164 0.130677 0.026226 0.004262 0.120211 Chicken 4 High 1
165 166 0.062274 0.013763 0.018192 0.286410 Chicken Breast 1 High 1
167 168 0.001469 0.450668 0.148653 0.010348 Dessert 1 High -1
169 170 0.208884 0.043308 0.300731 0.071967 Breakfast 4 High -1
170 171 0.148369 0.027640 0.031664 0.090241 One Dish Meal 2 High 1
173 174 0.062074 0.023322 0.187548 0.013981 Dessert 2 High 1
174 175 0.005658 0.010615 0.109606 0.054877 Breakfast 6 High 1
179 180 0.141163 0.052904 0.026564 0.354579 Chicken Breast 4 High 1
180 181 0.015359 0.022399 0.060283 0.002945 Potato 6 High 1
181 182 0.199871 0.043609 0.159689 0.015494 Dessert 6 High 1
182 183 0.240651 0.106752 0.024281 0.009577 Vegetable 4 High 1
185 186 0.435880 0.290635 0.004415 0.001266 Lunch/Snacks 4 High -1
186 187 0.177348 0.011030 0.101081 0.038419 Lunch/Snacks 1 High 1
187 188 0.099251 0.040442 0.034556 0.029723 Pork 4 High 1
188 189 0.062535 0.062237 0.339930 0.015081 Dessert 4 High 1
189 190 0.182231 0.044722 0.017278 0.043511 Breakfast 1 High 1
193 194 0.171229 0.006976 0.143401 0.022264 Chicken Breast 2 High 1
195 196 0.115673 0.070420 0.049627 0.063628 Lunch/Snacks 1 High 1
197 198 0.095637 0.018515 0.098341 0.073618 Chicken Breast 6 High 1
199 200 0.017957 0.005298 0.022454 0.242762 Meat 4 High 1
200 201 0.030827 0.020362 0.017126 0.012439 Vegetable 2 High 1
201 202 0.039172 0.023586 0.004795 0.021824 Vegetable 6 High 1
203 204 0.022720 0.019646 0.008068 0.024246 One Dish Meal 2 High 1
204 205 0.088851 0.009785 0.062567 0.079948 Breakfast 1 High 1
205 206 0.097589 0.064141 0.012331 0.002532 Lunch/Snacks 6 High 1
206 207 0.189809 0.002036 0.044832 0.512384 Pork 4 High -1
208 209 0.003465 0.084089 0.134952 0.037181 Pork 4 High 1
209 210 0.099251 0.040442 0.034556 0.029723 Dessert 2 High 1
210 211 0.400913 0.000207 0.033110 0.026530 Potato 1 High 1
211 212 0.106667 0.091536 0.266403 0.110414 Pork 4 High 1
212 213 0.099251 0.040442 0.034556 0.029723 Dessert 4 High 1
213 214 0.280849 0.010162 0.062643 0.103121 Meat 1 High 1
214 215 0.102957 0.018006 0.018953 0.345387 Chicken 6 High 1
216 217 0.008731 0.076566 0.012635 0.008972 Lunch/Snacks 2 High 1
217 218 0.444631 0.168122 0.108160 0.220250 Chicken Breast 4 High -1
218 219 0.057229 0.000943 0.225833 0.013375 Dessert 6 High 1
220 221 0.215295 0.044024 0.091186 0.070976 Breakfast 2 High 1
222 223 0.016508 0.009616 0.002131 0.001404 Lunch/Snacks 4 High 1
224 225 0.065987 0.036313 0.041711 0.010210 Potato 4 High 1
225 226 0.045997 0.010275 0.003273 0.158961 Pork 6 High 1
226 227 0.930179 0.012010 0.016441 0.077609 Pork 6 High -1
227 228 0.042896 0.299572 0.013320 0.063491 Breakfast 1 High 1
228 229 0.000000 0.034107 0.084792 0.240368 Lunch/Snacks 2 High 1
231 232 0.085224 0.061822 0.034708 0.027686 Vegetable 2 High 1
233 234 0.180170 0.003677 0.005404 0.114349 Chicken Breast 6 High 1
236 237 0.166164 0.101246 0.049627 0.009522 Dessert 6 High 1
238 239 0.200133 0.021588 0.060359 0.037539 Chicken 4 High 1
241 242 0.272500 0.105752 0.018724 0.007623 Potato 4 High 1
242 243 0.533424 0.137314 0.011265 0.110992 Chicken Breast 4 High -1
243 244 0.224979 0.210732 0.038286 0.000991 Potato 6 High 1
244 245 0.055161 0.021380 0.004491 0.000661 Potato 6 High 1
245 246 0.116089 0.052697 0.017126 0.052427 Potato 2 High 1
246 247 0.024719 0.031882 0.007155 0.009275 Vegetable 2 High 1
251 252 0.044355 0.097174 0.058228 0.016650 Lunch/Snacks 4 High 1
252 253 0.015100 0.013047 0.009286 0.006522 Vegetable 4 High 1
254 255 0.123495 0.003054 0.008373 0.056555 Chicken 4 High 1
256 257 0.234900 0.059070 0.043309 0.009852 Potato 2 High 1
257 258 0.197851 0.131809 0.008905 0.004954 Vegetable 1 High 1
259 260 0.232185 0.004921 0.027630 0.003468 Vegetable 2 High 1
261 262 0.047315 0.360452 0.015527 0.014669 Potato 2 High -1
263 264 0.159081 0.006524 0.050997 0.002559 Vegetable 6 High 1
264 265 0.124834 0.065235 0.009058 0.020586 Pork 2 High 1
265 266 0.215588 0.020155 0.033947 0.016237 Potato 1 High 1
266 267 0.150003 0.014386 0.169661 0.063381 Meat 4 High 1
267 268 0.608214 0.018722 0.031588 0.193747 Pork 4 High -1
268 269 0.143114 0.003413 0.136855 0.003880 Pork 1 High 1
270 271 0.157340 0.023963 0.036687 0.058977 Chicken Breast 4 High 1
271 272 0.149972 0.110522 0.300274 0.030768 Dessert 1 High 1
274 275 0.295447 0.009502 0.056782 0.005064 Lunch/Snacks 1 High 1
275 276 0.205587 0.036992 0.047039 0.018246 Lunch/Snacks 6 High 1
276 277 0.000213 0.003526 0.028543 0.000138 Vegetable 2 High 1
278 279 0.099251 0.040442 0.034556 0.029723 Lunch/Snacks 4 High 1
280 281 0.099251 0.040442 0.034556 0.029723 Meat 1 High 1
281 282 0.141042 0.025604 0.014842 0.024136 Vegetable 4 High 1
282 283 0.010844 0.010162 0.050845 0.036465 Vegetable 6 High 1
283 284 0.039437 0.061577 0.076191 0.240698 Pork 6 High 1
285 286 0.493900 0.142160 0.130005 0.000991 Lunch/Snacks 1 High -1
289 290 0.076696 0.048945 0.057162 0.032172 Meat 4 High 1
290 291 0.082509 0.096344 1.000000 0.009302 Dessert 2 High -1
292 293 0.646512 0.001452 0.057771 0.244964 Chicken Breast 1 High -1
294 295 0.083266 0.012538 0.001827 0.082067 Pork 4 High 1
295 296 0.054483 0.038820 0.047876 0.013072 Vegetable 2 High 1
296 297 0.167840 0.268086 0.039580 0.003000 Potato 6 High 1
299 300 0.439741 0.007768 0.002131 0.021026 Pork 1 High 1
301 302 0.101078 0.021494 0.023976 0.002697 Vegetable 2 High 1
304 305 0.061706 0.028583 0.016898 0.006963 Potato 1 High 1
305 306 0.223578 0.040744 0.012787 0.000358 Meat 2 High 1
306 307 0.587297 0.046306 0.040645 0.108790 Meat 4 High 1
308 309 0.025387 0.066027 0.015832 0.040401 Vegetable 6 High 1
309 310 0.199906 0.072569 0.000685 0.228011 Chicken Breast 1 High 1
310 311 0.312640 0.080130 0.009134 0.126954 Pork 4 High 1
312 313 0.362394 0.022625 0.126579 0.013540 One Dish Meal 2 High 1
316 317 0.057329 0.029620 0.032958 0.467250 Meat 4 High -1
317 318 0.031684 0.016026 0.001751 0.038915 Lunch/Snacks 4 High 1
318 319 0.162695 0.016309 0.014462 0.041502 Meat 4 High 1
319 320 0.140712 0.134901 0.043538 0.345828 Chicken Breast 2 High -1
321 322 0.026178 0.171987 0.046506 0.008999 Dessert 4 High 1
322 323 0.088638 0.265710 0.071929 0.024136 Breakfast 6 High 1
324 325 0.265456 0.180999 0.011265 0.020696 Dessert 4 High 1
326 327 0.099251 0.040442 0.034556 0.029723 Potato 4 High 1
327 328 0.274703 0.001565 0.003730 0.001101 Vegetable 4 High 1
328 329 0.193157 0.022248 0.068427 0.521054 Pork 6 High -1
329 330 0.190198 0.074511 0.027401 0.133587 Potato 4 High 1
331 332 0.333835 0.334075 0.013625 0.017723 Lunch/Snacks 2 High -1
332 333 0.007113 0.063896 0.041330 0.051024 Lunch/Snacks 4 High 1
334 335 0.183033 0.059824 0.010884 0.049428 Lunch/Snacks 4 High 1
337 338 0.047480 0.006919 0.039808 0.174923 Chicken 4 High 1
338 339 0.165998 0.049360 0.040189 0.052097 Potato 4 High 1
340 341 0.430604 0.023096 0.001218 0.001624 Lunch/Snacks 4 High 1
341 342 0.008087 0.193235 0.108160 0.168483 One Dish Meal 4 High -1
343 344 0.329726 0.094251 0.007383 0.025512 Breakfast 6 High 1
345 346 0.064463 0.089726 0.123991 0.216892 Pork 1 High 1
346 347 0.038044 0.110805 0.344954 0.003385 Dessert 6 High -1
347 348 0.143152 0.006410 0.007916 0.027851 Chicken 6 High 1
348 349 0.412634 0.077170 0.011722 0.081269 Pork 6 High 1
349 350 0.032393 0.722167 0.045441 0.109010 Potato 1 High -1
350 351 0.015651 0.028866 0.024814 0.005945 Breakfast 6 High 1
351 352 0.099251 0.040442 0.034556 0.029723 Potato 4 High 1
352 353 0.268770 0.007315 0.002892 0.058922 Pork 4 High 1
354 355 0.099251 0.040442 0.034556 0.029723 Pork 4 High 1
355 356 0.460299 0.007183 0.064469 0.002614 Vegetable 6 High 1
356 357 0.785465 0.008428 0.031588 0.085838 One Dish Meal 4 High -1
357 358 0.089206 0.037746 0.026184 0.250881 Meat 4 High 1
358 359 0.079515 0.028564 0.052900 0.194710 Chicken 1 High 1
359 360 0.046912 0.058561 0.018192 0.018632 Potato 2 High 1
360 361 0.292518 0.004770 0.011493 0.025622 Lunch/Snacks 6 High 1
361 362 0.346784 0.006410 0.043918 0.163006 Chicken Breast 4 High 1
362 363 0.179513 0.143932 0.096057 0.023723 Potato 6 High 1
366 367 0.011986 0.031769 0.034480 0.038639 Vegetable 4 High 1
367 368 0.042287 0.382040 0.121708 0.022402 Dessert 6 High -1
368 369 0.198997 0.017723 0.033415 0.023007 Chicken Breast 1 High 1
369 370 0.068620 0.017101 0.062110 0.027796 Meat 6 High 1
371 372 0.089409 0.035634 0.038590 0.000908 Vegetable 6 High 1
372 373 0.099251 0.040442 0.034556 0.029723 Vegetable 2 High 1
373 374 0.301855 0.031279 0.281930 0.058042 Pork 4 High -1
375 376 0.091391 0.021965 0.088979 0.002174 Vegetable 2 High 1
376 377 0.099251 0.040442 0.034556 0.029723 Pork 6 High 1
377 378 0.062656 0.021927 0.006850 0.203490 Chicken Breast 4 High 1
378 379 0.081136 0.081393 0.313822 0.011751 Dessert 1 High 1
379 380 0.026068 0.042384 0.062186 0.048547 Meat 6 High 1
380 381 0.149404 0.121684 0.022073 0.019705 Potato 2 High 1
382 383 0.094918 0.056392 0.100548 0.020613 Chicken 1 High 1
383 384 0.243817 0.085616 0.034556 0.610717 Chicken Breast 6 High -1
385 386 0.025820 0.012104 0.065307 0.002009 Beverages 2 High 1
388 389 0.099251 0.040442 0.034556 0.029723 Lunch/Snacks 4 High 1
389 390 0.087905 0.089406 0.011950 0.013540 Vegetable 4 High 1
391 392 0.037830 0.007617 0.069189 0.024439 Potato 6 High 1
395 396 0.031251 0.028281 0.040417 0.053088 Breakfast 1 High 1
396 397 0.022100 0.021211 0.094839 0.057436 Potato 4 High 1
397 398 0.705882 0.004582 0.016060 0.191601 Chicken 2 High -1
398 399 0.044104 0.027753 0.022835 0.044969 Pork 4 High 1
404 405 0.048526 0.128226 0.011189 0.019127 Meat 4 High 1
405 406 0.099251 0.040442 0.034556 0.029723 Vegetable 4 High 1
406 407 0.058396 0.079922 0.014995 0.085590 Meat 4 High 1
411 412 0.212453 0.027621 0.075126 0.008587 Dessert 6 High 1
412 413 0.081845 0.006260 0.009134 0.014861 Vegetable 6 High 1
413 414 0.051857 0.222798 0.005633 0.017256 Potato 4 High 1
415 416 0.161267 0.067064 0.015071 0.056858 Meat 4 High 1
417 418 0.246233 0.016874 0.014919 0.002559 Vegetable 4 High 1
418 419 0.629808 0.007334 0.013853 0.123129 Chicken Breast 1 High -1
419 420 0.184551 0.016667 0.015756 0.105873 Chicken Breast 4 High 1
421 422 0.114269 0.048700 0.007840 0.055730 Meat 4 High 1
422 423 0.082722 0.036709 0.042853 0.006715 Pork 4 High 1
424 425 0.026202 0.225494 0.003045 0.048960 Lunch/Snacks 4 High 1
425 426 0.434641 0.009634 0.125818 0.276475 Meat 2 High -1
427 428 0.099251 0.040442 0.034556 0.029723 Vegetable 4 High 1
428 429 0.566677 0.190577 0.021008 0.040924 Meat 1 High -1
432 433 0.344076 0.069044 0.043005 0.046015 Chicken Breast 4 High 1
433 434 0.016790 0.123777 0.012255 0.026282 Meat 4 High 1
435 436 0.104943 0.041385 0.232836 0.032447 Lunch/Snacks 4 High 1
436 437 0.249870 0.080488 0.012255 0.052813 One Dish Meal 6 High 1
438 439 0.052218 0.013914 0.046278 0.110744 One Dish Meal 4 High 1
439 440 0.039210 0.028470 0.013701 0.106231 Chicken 1 High 1
440 441 0.101684 0.042308 0.226138 0.021356 Dessert 6 High 1
442 443 0.037214 0.114840 0.059218 0.152631 One Dish Meal 4 High 1
443 444 0.246621 0.046947 0.025194 0.044336 Meat 6 High 1
445 446 0.128777 0.028583 0.070102 0.006853 Dessert 2 High 1
446 447 0.043230 0.017251 0.004034 0.234808 Chicken Breast 2 High 1
449 450 0.049603 0.074662 0.066981 0.040153 Pork 4 High 1
450 451 0.062845 1.000000 0.044071 0.013788 Potato 4 High -1
451 452 0.530784 0.027715 0.043386 0.082343 Lunch/Snacks 4 High 1
454 455 0.006225 0.042026 0.039047 0.050556 Potato 6 High 1
455 456 0.099251 0.040442 0.034556 0.029723 Pork 6 High 1
456 457 0.098614 0.056355 0.021236 0.077251 Breakfast 4 High 1
457 458 0.274135 0.084504 0.017887 0.009467 Potato 4 High 1
458 459 0.089192 0.170855 0.028848 0.236157 One Dish Meal 1 High 1
459 460 0.249729 0.078527 0.011417 0.021411 Potato 6 High 1
460 461 0.405854 0.000528 0.104202 0.003908 One Dish Meal 4 High 1
461 462 0.120153 0.011595 0.014538 0.069298 Vegetable 2 High 1
462 463 0.306813 0.005600 0.037753 0.086306 Breakfast 4 High 1
464 465 0.037318 0.040800 0.001751 0.030934 Vegetable 1 High 1
467 468 0.009997 0.002451 0.502512 0.007045 Dessert 6 High -1
471 472 0.427304 0.011275 0.078779 0.032998 Pork 4 High 1
472 473 0.175937 0.011746 0.054498 0.018219 Dessert 4 High 1
473 474 0.023256 0.020739 0.040417 0.111157 Pork 1 High 1
474 475 0.121781 0.028903 0.205206 0.176547 Chicken Breast 2 High 1
475 476 0.018869 0.006957 0.055183 0.002972 Vegetable 4 High 1
476 477 0.190487 0.004940 0.012863 0.021907 Vegetable 4 High 1
478 479 0.187882 0.049209 0.024052 0.053446 Chicken 6 High 1
479 480 0.180717 0.117706 0.009591 0.029860 Lunch/Snacks 1 High 1
481 482 0.135178 0.195007 0.037753 0.002917 Vegetable 6 High 1
482 483 0.316804 0.018628 0.294185 0.000991 Dessert 4 High -1
485 486 0.047851 0.037840 0.139976 0.010871 Breakfast 4 High 1
486 487 0.002550 0.102114 0.006850 0.039025 Lunch/Snacks 1 High 1
487 488 0.445674 0.065499 0.005633 0.471213 Meat 2 High -1
494 495 0.015899 0.034333 0.001066 0.004348 Vegetable 4 High 1
495 496 0.405318 0.090914 0.005861 0.050363 Pork 2 High 1
496 497 0.089478 0.043440 0.544299 0.008862 Dessert 2 High -1
497 498 0.120608 0.138125 0.135180 0.005449 Dessert 4 High 1
499 500 0.114551 0.036596 0.005176 0.034319 Meat 1 High 1
502 503 0.382123 0.052678 0.030065 0.078847 One Dish Meal 4 High 1
503 504 0.182087 0.189804 0.044680 0.026420 Vegetable 4 High 1
506 507 0.294666 0.100379 0.088446 0.017613 Vegetable 4 High 1
508 509 0.046702 0.044986 0.094154 0.040979 Potato 1 High 1
511 512 0.130980 0.045419 0.021312 0.019320 Potato 4 High 1
512 513 0.002495 0.008371 0.088370 0.029585 Vegetable 4 High 1
514 515 0.011088 0.002772 0.103973 0.033273 Breakfast 1 High 1
516 517 0.090847 0.073569 0.022530 0.063408 Chicken 4 High 1
517 518 0.078992 0.020155 0.043614 0.003688 Vegetable 4 High 1
520 521 0.125047 0.044288 0.003730 0.216012 Chicken Breast 1 High 1
521 522 0.206771 0.369106 0.039884 0.063601 One Dish Meal 4 High -1
522 523 0.155730 0.040065 0.092251 0.398255 Chicken Breast 2 High -1
524 525 0.185476 0.025321 0.006241 0.036851 Chicken Breast 4 High 1
527 528 0.003620 0.004318 0.000533 0.202059 Pork 6 High 1
529 530 0.154326 0.343049 0.277516 0.033108 One Dish Meal 6 High -1
530 531 0.099251 0.040442 0.034556 0.029723 Vegetable 1 High 1
532 533 0.183959 0.001810 0.002360 0.008724 Potato 1 High 1
534 535 0.099251 0.040442 0.034556 0.029723 Chicken 2 High 1
535 536 0.237158 0.009634 0.008525 0.035007 Vegetable 4 High 1
536 537 0.302653 0.000000 0.063404 0.021549 Chicken Breast 4 High 1
538 539 0.099251 0.040442 0.034556 0.029723 Vegetable 4 High 1
541 542 0.176501 0.004054 0.000913 0.135238 Lunch/Snacks 2 High 1
542 543 0.017568 0.075284 0.061653 0.050391 Potato 4 High 1
543 544 0.077856 0.025283 0.003349 0.103754 Potato 4 High 1
545 546 0.099251 0.040442 0.034556 0.029723 Chicken Breast 6 High 1
547 548 0.590302 0.084146 0.050693 0.090764 Pork 4 High 1
548 549 0.495738 0.057618 0.043918 0.084297 Lunch/Snacks 6 High 1
549 550 0.162867 0.018628 0.013929 0.018081 Potato 4 High 1
550 551 0.319013 0.000038 0.013092 0.033823 Potato 4 High 1
552 553 0.083001 0.060635 0.010808 0.002752 Vegetable 2 High 1
553 554 0.073252 0.105224 0.091110 0.003275 Chicken Breast 4 High 1
557 558 0.011986 0.005091 0.230705 0.013623 Breakfast 2 High 1
559 560 0.075544 0.045872 0.040493 0.003633 Breakfast 2 High 1
563 564 0.007530 0.058768 0.005556 0.192839 Chicken Breast 1 High 1
564 565 0.120119 0.051566 0.095448 0.269402 One Dish Meal 4 High 1
565 566 0.128061 0.153642 0.028619 0.062583 One Dish Meal 4 High 1
568 569 0.144239 0.029846 0.004415 0.027521 One Dish Meal 4 High 1
569 570 0.182723 0.005996 0.006318 0.154640 Chicken 1 High 1
571 572 0.075895 0.009107 0.022606 0.052015 Vegetable 4 High 1
572 573 0.212570 0.030694 0.032273 0.026393 Chicken Breast 1 High 1
577 578 0.008022 0.033843 0.053966 0.026778 Potato 2 High 1
578 579 0.199661 0.041743 0.028619 0.025374 Potato 4 High 1
582 583 0.155427 0.036973 0.031588 0.014779 Vegetable 6 High 1
585 586 0.280715 0.129490 0.007383 0.184115 Chicken Breast 2 High 1
587 588 0.000489 0.112823 0.049931 0.096323 Pork 4 High 1
588 589 0.084876 0.023662 0.021312 0.064289 Pork 2 High 1
589 590 0.275849 0.076830 0.036611 0.121175 Meat 6 High 1
590 591 0.017874 0.142084 0.376922 0.078077 Pork 6 High -1
592 593 0.029905 0.014518 0.646598 0.026007 Dessert 1 High -1
593 594 0.128082 0.021908 0.042016 0.094149 Pork 2 High 1
595 596 0.105566 0.036992 0.078627 0.078077 Pork 2 High 1
599 600 0.310544 0.012199 0.054498 0.114487 Pork 2 High 1
600 601 0.170341 0.061879 0.032197 0.039107 Potato 6 High 1
603 604 0.035039 0.061163 0.087000 0.008752 Vegetable 4 High 1
604 605 0.003228 0.012010 0.063556 0.136421 Meat 1 High 1
605 606 0.033907 0.002640 0.058761 0.022402 Chicken 4 High 1
606 607 0.026708 0.040310 0.026640 0.008587 Vegetable 4 High 1
607 608 0.017792 0.145157 0.038666 0.018081 Pork 4 High 1
610 611 0.066300 0.147552 0.003045 0.004458 Potato 1 High 1
612 613 0.268539 0.013198 0.015756 0.027328 Lunch/Snacks 1 High 1
614 615 0.080568 0.018006 0.076039 0.007926 Potato 4 High 1
616 617 0.131558 0.118630 0.150708 0.495542 Pork 4 High -1
620 621 0.008947 0.091254 0.014995 0.083884 Lunch/Snacks 4 High 1
621 622 0.150908 0.240276 0.027401 0.000193 Dessert 2 High 1
622 623 0.045350 0.014914 0.018344 0.009797 Vegetable 1 High 1
623 624 0.081380 0.018175 0.006089 0.024961 Potato 6 High 1
625 626 0.038880 0.165030 0.129015 0.004266 Breakfast 2 High 1
626 627 0.187163 0.074021 0.013244 0.006330 Chicken Breast 6 High 1
628 629 0.249251 0.009710 0.015908 0.029365 One Dish Meal 6 High 1
630 631 0.134297 0.095156 0.013929 0.089883 Lunch/Snacks 1 High 1
631 632 0.103129 0.076849 0.033110 0.002917 Chicken Breast 6 High 1
632 633 0.036413 0.105149 0.000457 0.020778 Vegetable 2 High 1
633 634 0.186423 0.100605 0.008297 0.023145 Vegetable 4 High 1
635 636 0.175731 0.091932 0.014157 0.019870 Lunch/Snacks 1 High 1
636 637 0.698307 0.047229 0.004567 0.148613 One Dish Meal 1 High -1
638 639 0.110745 0.002602 0.081824 0.309996 Meat 1 High 1
640 641 0.047662 0.033485 0.064774 0.018769 Vegetable 2 High 1
642 643 0.554023 0.080224 0.001751 0.133587 Meat 4 High -1
644 645 0.008954 0.061653 0.009971 0.002009 Potato 2 High 1
645 646 0.444383 0.119893 0.062490 0.051904 Lunch/Snacks 1 High 1
647 648 0.186664 0.014122 0.059446 0.112120 Pork 4 High 1
650 651 0.092681 0.132921 0.049551 0.063986 Potato 4 High 1
652 653 0.000138 0.057618 0.079464 0.000991 Beverages 6 High 1
653 654 0.252186 0.015611 0.006850 0.001871 Potato 6 High 1
654 655 0.000155 0.035427 0.441391 0.045244 Dessert 2 High -1
655 656 0.100400 0.068346 0.480515 0.019320 Dessert 4 High -1
657 658 0.046434 0.049567 0.034785 0.019567 Potato 4 High 1
658 659 0.005757 0.116405 0.044908 0.028402 Breakfast 1 High 1
659 660 0.129204 0.029016 0.057771 0.048712 One Dish Meal 4 High 1
660 661 0.099358 0.053923 0.386665 0.021219 Dessert 6 High 1
661 662 0.248349 0.000302 0.002436 0.052290 Dessert 1 High 1
665 666 0.221527 0.001263 0.271959 0.035667 Vegetable 6 High -1
666 667 0.034499 0.018156 0.185416 0.046703 Vegetable 2 High 1
667 668 0.437652 0.094459 0.127797 0.053501 Pork 2 High 1
668 669 0.118319 0.044797 0.448090 0.001128 Dessert 1 High -1
670 671 0.165782 0.023963 0.010123 0.142834 Chicken 1 High 1
672 673 0.022675 0.042308 0.037525 0.004899 Vegetable 2 High 1
673 674 0.005386 0.022361 0.038590 0.010320 Vegetable 4 High 1
674 675 0.099251 0.040442 0.034556 0.029723 Pork 4 High 1
675 676 0.246639 0.028451 0.031664 0.006192 Potato 6 High 1
678 679 0.053636 0.039462 0.078170 0.212984 Meat 1 High 1
679 680 0.061534 0.042535 0.030674 0.019375 One Dish Meal 2 High 1
681 682 0.116743 0.010785 0.077485 1.000000 Chicken Breast 6 High -1
683 684 0.099251 0.040442 0.034556 0.029723 Potato 1 High 1
685 686 0.025177 0.014518 0.029989 0.100369 Vegetable 6 High 1
687 688 0.085754 0.130828 0.060131 0.031071 Dessert 4 High 1
688 689 0.133925 0.260224 0.059370 0.021852 Potato 6 High 1
689 690 0.166735 0.114840 0.007916 0.012247 Potato 4 High 1
690 691 0.095741 0.041894 0.011417 0.049262 Chicken 1 High 1
691 692 0.019536 0.201908 0.014462 0.002202 Vegetable 4 High 1
693 694 0.011281 0.322121 0.396103 0.017256 Dessert 4 High -1
694 695 0.463978 0.286978 0.001066 0.007045 One Dish Meal 2 High -1
696 697 0.583189 0.078263 0.083346 0.090048 Meat 4 High -1
698 699 0.029303 0.087407 0.021084 0.013981 Vegetable 2 High 1
699 700 0.216235 0.039273 0.002436 0.048822 Chicken Breast 6 High 1
700 701 0.038653 0.029243 0.032882 0.071830 Lunch/Snacks 4 High 1
702 703 0.179354 0.009144 0.088902 0.090764 Chicken 6 High 1
707 708 0.102905 0.038877 0.048181 0.034814 Pork 6 High 1
709 710 0.111068 0.027961 0.000076 0.018824 Lunch/Snacks 6 High 1
710 711 0.071383 0.002809 0.033871 0.041419 One Dish Meal 6 High 1
711 712 0.099251 0.040442 0.034556 0.029723 Lunch/Snacks 4 High 1
712 713 0.099251 0.040442 0.034556 0.029723 Pork 6 High 1
713 714 0.247833 0.012915 0.005709 0.047446 Meat 4 High 1
714 715 0.359311 0.058146 0.028086 0.014779 Potato 4 High 1
716 717 0.038966 0.009182 0.009743 0.047006 Pork 6 High 1
717 718 0.343942 0.264315 0.038590 0.002092 One Dish Meal 1 High 1
718 719 0.314883 0.084410 0.068199 0.039272 Potato 4 High 1
719 720 0.146466 0.305586 0.004795 0.021274 Lunch/Snacks 6 High 1
720 721 0.082234 0.152341 0.002207 0.063986 Breakfast 2 High 1
721 722 0.546521 0.127849 0.102146 0.016072 Dessert 6 High -1
722 723 0.368189 0.234035 0.016517 0.139916 One Dish Meal 6 High -1
723 724 0.335345 0.004977 0.035850 0.008476 Pork 4 High 1
724 725 0.090782 0.001791 0.056325 0.035997 Pork 4 High 1
725 726 0.009402 0.015592 0.052519 0.049950 Meat 4 High 1
726 727 0.137350 0.182281 0.065383 0.143109 Pork 4 High 1
728 729 0.281633 0.009314 0.009438 0.086939 Lunch/Snacks 4 High 1
731 732 0.245066 0.043873 0.063784 0.150017 Chicken 2 High 1
732 733 0.422593 0.061295 0.012635 0.020310 Potato 4 High 1
733 734 0.073950 0.016648 0.008297 0.010375 Chicken Breast 1 High 1
734 735 0.018669 0.116405 0.066677 0.035474 Pork 4 High 1
737 738 0.236315 0.033353 0.728726 0.004513 Dessert 4 High -1
738 739 0.061634 0.499745 0.001066 0.124642 One Dish Meal 6 High -1
739 740 0.030438 0.082373 0.029609 0.084627 Lunch/Snacks 4 High 1
740 741 0.482764 0.003959 0.030446 0.054354 Chicken Breast 6 High 1
741 742 0.031615 0.096646 0.073451 0.005367 Dessert 4 High 1
742 743 0.359655 0.139445 0.003958 0.096158 Meat 1 High 1
743 744 0.004422 0.129659 0.027630 0.031319 Lunch/Snacks 1 High 1
744 745 0.090472 0.099625 0.009591 0.053748 Potato 6 High 1
745 746 0.044369 0.070269 0.015832 0.086884 Pork 2 High 1
749 750 0.099251 0.040442 0.034556 0.029723 Dessert 4 High 1
752 753 0.474030 0.035747 0.060283 0.107166 One Dish Meal 4 High 1
753 754 0.453365 0.045985 0.200563 0.236102 Pork 4 High -1
754 755 0.023934 0.143404 0.013016 0.272815 One Dish Meal 2 High -1
755 756 0.271888 0.007334 0.045060 0.063738 Chicken Breast 4 High 1
759 760 0.302340 0.014461 0.029380 0.096653 Pork 6 High 1
761 762 0.067997 0.422576 0.022530 0.003468 Potato 2 High -1
762 763 0.128915 0.006392 0.137464 0.069876 One Dish Meal 1 High 1
763 764 0.087970 0.209374 0.109986 0.005972 Pork 1 High 1
765 766 0.099251 0.040442 0.034556 0.029723 Pork 1 High 1
766 767 0.074897 0.001112 0.034252 0.001266 Vegetable 6 High 1
767 768 0.116010 0.060126 0.060816 0.075710 One Dish Meal 1 High 1
770 771 0.309508 0.104659 0.058076 0.050253 Dessert 2 High 1
773 774 0.205656 0.065499 0.035698 0.105295 Chicken Breast 1 High 1
775 776 0.437157 0.020193 0.016593 0.143081 Pork 4 High 1
778 779 0.082987 0.007956 0.058456 0.027163 Lunch/Snacks 6 High 1
779 780 0.004167 0.021003 0.056554 0.074004 Meat 6 High 1
781 782 0.711639 0.064481 0.011037 0.027576 Potato 1 High -1
782 783 0.015145 0.035050 0.012483 0.502614 Pork 1 High -1
783 784 0.182087 0.014159 0.052748 0.069380 Meat 4 High 1
785 786 0.008156 0.102717 0.004719 0.001624 Breakfast 6 High 1
787 788 0.034651 0.307095 0.003045 0.015632 Potato 6 High 1
788 789 0.024629 0.097739 0.074517 0.041557 Meat 4 High 1
789 790 0.012217 0.174230 0.120414 0.028264 One Dish Meal 4 High 1
792 793 0.224263 0.119968 0.039884 0.143604 One Dish Meal 6 High 1
793 794 0.217845 0.037652 0.017811 0.071857 Lunch/Snacks 4 High 1
794 795 0.165654 0.011237 0.041254 0.078462 Meat 4 High 1
795 796 0.014378 0.047606 0.014766 0.040236 Chicken Breast 2 High 1
796 797 0.228968 0.029261 0.005785 0.051739 Chicken 4 High 1
797 798 0.032452 0.025227 0.018800 0.008917 Potato 4 High 1
798 799 0.034647 0.193084 0.009438 0.092223 Potato 1 High 1
801 802 0.371039 0.025359 0.086847 0.376073 Pork 6 High -1
803 804 0.343783 0.096740 0.021388 0.069490 Lunch/Snacks 1 High 1
806 807 0.033157 0.070420 0.029837 0.003413 Potato 6 High 1
807 808 0.025266 0.029375 0.044908 0.012962 Lunch/Snacks 6 High 1
811 812 0.362490 0.012180 0.080073 0.028264 Vegetable 4 High 1
812 813 0.189468 0.021682 0.002588 0.000000 Potato 2 High 1
813 814 0.428636 0.000094 0.109073 0.009687 Lunch/Snacks 4 High 1
816 817 0.184251 0.000094 0.033415 0.015302 One Dish Meal 6 High 1
818 819 0.071638 0.158713 0.037905 0.119716 One Dish Meal 6 High 1
819 820 0.206513 0.096250 0.137464 0.016265 Dessert 4 High 1
820 821 0.002578 0.073455 0.039580 0.001761 Vegetable 4 High 1
821 822 0.151063 0.072437 0.020627 0.043208 Pork 1 High 1
822 823 0.072959 0.046871 0.012559 0.016870 One Dish Meal 2 High 1
823 824 0.466748 0.016158 0.025270 0.006137 Pork 2 High 1
824 825 0.085258 0.084183 0.020018 0.054877 Chicken Breast 1 High 1
825 826 0.004164 0.069949 0.025727 0.000771 Vegetable 1 High 1
826 827 0.078589 0.076057 0.039351 0.012522 Potato 1 High 1
827 828 0.024502 0.072098 0.021845 0.068087 Meat 1 High 1
828 829 0.142608 0.033579 0.009286 0.020641 Potato 6 High 1
829 830 0.314064 0.150663 0.014005 0.122083 One Dish Meal 1 High 1
830 831 0.218059 0.431079 0.008297 0.011394 Potato 4 High -1
831 832 0.232154 0.040442 0.029837 0.065665 Lunch/Snacks 1 High 1
832 833 0.008080 0.093874 0.037753 0.015219 Vegetable 4 High 1
833 834 0.029554 0.059315 0.009591 0.018494 Lunch/Snacks 6 High 1
834 835 0.037307 0.080262 0.007840 0.005394 Vegetable 4 High 1
837 838 0.004298 0.076906 0.004415 0.009385 Vegetable 4 High 1
838 839 0.250324 0.005241 0.007612 0.078572 Meat 4 High 1
839 840 0.117063 0.000716 0.017354 0.029035 Chicken 4 High 1
840 841 0.029172 0.053319 0.075278 0.114294 Chicken 6 High 1
842 843 0.097303 0.216105 0.019029 0.077361 Lunch/Snacks 6 High 1
843 844 0.088022 0.297687 0.016517 0.009550 Potato 4 High 1
844 845 0.277015 0.366202 0.349673 0.011091 Dessert 4 High -1
848 849 0.022028 0.011520 0.001979 0.080719 Meat 2 High 1
851 852 0.099251 0.040442 0.034556 0.029723 Lunch/Snacks 4 High 1
855 856 0.049338 0.011576 0.004034 0.013760 Pork 4 High 1
856 857 0.124796 0.183224 0.004110 0.058097 Lunch/Snacks 2 High 1
857 858 0.038945 0.021626 0.013092 0.031924 Vegetable 2 High 1
858 859 0.133626 0.024246 0.035089 0.038337 Pork 4 High 1
861 862 0.566622 0.017704 0.038895 0.675831 Chicken Breast 6 High -1
864 865 0.019784 0.014631 0.037601 0.010320 Vegetable 4 High 1
865 866 0.099251 0.040442 0.034556 0.029723 Lunch/Snacks 6 High 1
868 869 0.103704 0.019533 0.106637 0.013595 Lunch/Snacks 4 High 1
870 871 0.506282 0.163050 0.005937 0.001596 Potato 1 High -1
871 872 0.152109 0.010539 0.005937 0.164272 One Dish Meal 2 High 1
872 873 0.079195 0.017402 0.003197 0.156319 One Dish Meal 6 High 1
873 874 0.055460 0.066479 0.024509 0.009660 Potato 6 High 1
874 875 0.336123 0.014518 0.022835 0.134963 One Dish Meal 2 High 1
875 876 0.048701 0.072588 0.022454 0.027879 Potato 4 High 1
876 877 0.008627 0.000830 0.157025 0.144650 Pork 2 High -1
878 879 0.030325 0.034974 0.039580 0.274218 Meat 6 High 1
881 882 0.075403 0.005958 0.044223 0.035089 Vegetable 4 High 1
886 887 0.010403 0.072343 0.000685 0.096874 Vegetable 2 High 1
887 888 0.590491 0.037633 0.050997 0.069600 Chicken Breast 2 High 1
888 889 0.388548 0.160863 0.012787 0.032805 Pork 4 High 1
890 891 0.099251 0.040442 0.034556 0.029723 Meat 4 High 1
892 893 0.046179 0.044194 0.033871 0.063876 Vegetable 4 High 1
895 896 0.106306 0.237844 0.287334 0.007596 Dessert 4 High -1
896 897 0.099251 0.040442 0.034556 0.029723 Chicken 6 High 1
898 899 0.221569 0.059447 0.017430 0.157255 Meat 2 High 1
900 901 0.040108 0.037538 0.074136 0.008284 Vegetable 2 High 1
901 902 0.031822 0.002489 0.027249 0.000110 Beverages 6 High 1
902 903 0.268278 0.046928 0.012255 0.061784 Pork 4 High 1
903 904 0.097888 0.018288 0.013320 0.307051 Meat 4 High 1
904 905 0.154542 0.181150 0.034937 0.018109 Pork 2 High 1
907 908 0.802575 0.014027 0.027478 0.094479 One Dish Meal 1 High -1
908 909 0.037607 0.097362 0.019257 0.012384 Lunch/Snacks 1 High 1
909 910 0.298919 0.087558 0.020323 0.063436 Lunch/Snacks 1 High 1
910 911 0.025669 0.005581 0.008905 0.000605 Potato 6 High 1
911 912 0.099251 0.040442 0.034556 0.029723 Dessert 6 High 1
914 915 0.079567 0.114124 0.000000 0.014201 Potato 6 High 1
915 916 0.042855 0.042045 0.004567 0.003605 Potato 4 High 1
918 919 0.099251 0.040442 0.034556 0.029723 Pork 6 High 1
919 920 0.395204 0.019495 0.043309 0.084847 Meat 6 High 1
920 921 0.129111 0.248440 0.124981 0.013100 Chicken 4 High 1
922 923 0.009467 0.058655 0.027325 0.023833 Potato 4 High 1
923 924 0.111492 0.053527 0.059218 0.034319 Meat 2 High 1
926 927 0.092606 0.056920 0.103669 0.101057 Meat 2 High 1
927 928 0.087065 0.036200 0.085325 0.006467 One Dish Meal 2 High 1
928 929 0.001972 0.090273 0.228726 0.068032 Meat 4 High 1
930 931 0.209018 0.065706 0.041711 0.026943 Lunch/Snacks 4 High 1
931 932 0.037865 0.123815 0.442533 0.016292 Dessert 1 High -1
933 934 0.016381 0.009993 0.006546 0.047941 Chicken Breast 4 High 1
934 935 0.039052 0.013368 0.040037 0.036878 Vegetable 4 High 1
935 936 0.090214 0.113313 0.551682 0.013953 Dessert 4 High -1
936 937 0.056685 0.002998 0.057543 0.133064 Pork 4 High 1
937 938 0.043419 0.085692 0.047572 0.028732 Dessert 1 High 1
938 939 0.099251 0.040442 0.034556 0.029723 Pork 4 High 1
941 942 0.064032 0.158204 0.014995 0.061647 Chicken Breast 4 High 1
943 944 0.099251 0.040442 0.034556 0.029723 Potato 2 High 1
944 945 0.327475 0.055412 0.027097 0.038172 Pork 2 High 1
945 946 0.091701 0.067384 0.007307 0.022209 Potato 6 High 1
In [29]:
# Set the background color to black
plt.style.use('dark_background')

# Create a figure with a specified size
plt.figure(figsize=(12, 8))

# Scatter plot of original vs. outlier status
sns.scatterplot(data=df_scaled, x='calories', y='protein', hue='outlier', 
                palette={1: '#F0F8FF', -1: '#FFE4E1'})  # Use specified colors

# Set the title of the plot
plt.title('Outliers Detection Using Isolation Forest', fontsize=18, weight='bold', color='white')

# Set the x-axis label
plt.xlabel('Calories', fontsize=14, color='white')

# Set the y-axis label
plt.ylabel('Protein', fontsize=14, color='white')

# Set the legend title and location
plt.legend(title='Outlier Status', loc='upper right', fontsize=12, facecolor='#F0F8FF')

# Show the plot
plt.show()
No description has been provided for this image

🌟 Exploratory Data Analysis (EDA) Summary 🌟¶

1. Dataset Overview¶

We embarked on our analytical journey by surveying the landscape of our dataset understanding its dimensions, with the number of rows and columns setting the stage for our exploration.

2. Statistical Snapshot 📈¶

Dived deep into the numbers to unveil essential metrics like the mean, median, and standard deviation. This statistical deep-dive was crucial in mapping out the data's distribution and variability, giving us a clear picture of what to expect.

3. Data Type Insights 🧐¶

Peered into the data's soul by categorizing variable types. This step was key to shaping our approach, allowing us to apply tailored processing strategies to each data type.

4. Null Values Handling 🚫¶

Tackled missing data with a well-orchestrated strategy:

  • Initial Imputation: Filled the gaps with column means, ensuring our dataset remained intact and analysis-ready.

  • Log Transformation: Transformed certain columns using a logarithmic approach to smooth out skewness, normalize the data, and stabilize variance.

  • Re-Transformation and Cleaning: Post-transformation, re-imputed remaining null values to preserve data integrity. Reversed the log transformation to return to the original data scale, tidying up intermediate columns used during this process.

5. Duplicate Handling 🔍¶

Safeguarded data purity by rooting out duplicates. This meticulous step was pivotal in maintaining unique data points and ensuring our analysis was based on clean, non-redundant information.

6. Data Visualization 🎨¶

Brought the data to life with a suite of visualizations to reveal hidden patterns and insights:

  • Numeric Columns: Explored distributions and trends with histograms and box plots, illuminating the spread and central tendencies of our numerical features.

  • Categorical Columns: Unveiled the distribution of categorical variables through bar charts and count plots, shedding light on the frequency and spread of categories.

  • Relationships:

    • Correlation Matrix: Mapped out the interrelationships between numeric features to identify dependencies and correlations.

    • Scatter Plots: Illustrated pairwise relationships, uncovering both linear and non-linear correlations between variables.

    • Box Plots: Highlighted distribution ranges and potential outliers, offering a clear view of data dispersion.

    • Pair Plots: Provided a panoramic view of relationships across all numeric features, offering a holistic perspective.

    • Count Plots: Showcased the frequency of categorical variable occurrences, emphasizing distribution trends.

7. Skewness Handling 📉¶

Addressed skewness to bring balance to our data:

  • Skewness Calculation: Measured skewness before and after applying transformations to gauge data normalization.

  • Transformations:

    • Box-Cox Transformation: Applied to stabilize variance and reduce skewness for positive values.

    • Log Transformation: Utilized for handling zeros and reducing skewness in non-positive values.

    • Styled Tables: Created visually appealing tables to compare skewness pre- and post-transformation, showcasing the effectiveness of our adjustments.

8. Outlier Handling 🚀¶

Handled outliers with precision using the Isolation Forest algorithm:

  • Outlier Detection: Implemented Isolation Forest to pinpoint anomalies. Outliers were tagged for further scrutiny.

  • Visualization: Displayed outliers on scatter plots to visually distinguish them from the primary data, enhancing our understanding of their impact.

This summary reflects the meticulous and creative approach taken during the EDA process, providing a vivid and engaging overview of data cleaning, transformation, and visualization techniques used to prepare the dataset for advanced analysis and modeling.

3 | Feature Engineering

3.1 | Categorical Features Handling

In [22]:
# Initialize the label encoder
label_encoder = LabelEncoder()

# Encode the 'category' column
df['category'] = label_encoder.fit_transform(df['category'])

# Encode the 'high_traffic' column
df['high_traffic'] = label_encoder.fit_transform(df['high_traffic'])

3.2 | Scaling Features

In [23]:
df1 = df.copy()
# Initialize the scaler
scaler = MinMaxScaler()

# Define features to scale (include newly encoded columns if necessary)
features_to_scale = ['calories', 'carbohydrate', 'sugar', 'protein', 'recipe'] 

# Apply scaling
df1[features_to_scale] = scaler.fit_transform(df1[features_to_scale])
df1.head()
Out[23]:
recipe calories carbohydrate sugar protein category servings high_traffic
0 0.000000 0.099251 0.040442 0.034556 0.029723 8 6 0
1 0.001058 0.012162 0.072645 0.004947 0.002532 9 4 0
3 0.003175 0.033343 0.057561 0.293956 0.000055 0 4 0
5 0.005291 0.237798 0.006467 0.012483 0.148420 7 2 0
8 0.008466 0.185273 0.007070 0.025575 0.010430 8 6 0

4 | Data Separation

In [24]:
# Define features (X) and target (y)
X = df1.drop(columns=['high_traffic'])
y = df1['high_traffic']

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

# Display the shapes of the resulting datasets
print(f"Training set shape: {X_train.shape}")
print(f"Testing set shape: {X_test.shape}")

Training set shape: (430, 7)
Testing set shape: (144, 7)

5 | Models Building & Evaluation

Problem Type: Classification¶

The task at hand is a classification problem where we aim to predict whether a recipe will drive high traffic ("High") or not. This involves distinguishing between two classes based on various features associated with each recipe.

Models Chosen:¶

1.Decision Tree Classifier¶

2. Random Forest Classifier¶

Model # 1: Random Forest Classifier

Reasons for Selection:¶

Enhanced Accuracy:¶

Improves upon Decision Trees by aggregating predictions from multiple trees, leading to better accuracy and reduced overfitting.

Complex Pattern Recognition:¶

Handles intricate patterns and interactions within the data, modeling complex relationships between features and traffic.

Stability and Robustness:¶

Offers greater stability and robustness by mitigating the impact of outliers and noise, ensuring reliable predictions.

Feature Importance:¶

Provides a comprehensive view of feature importance by averaging scores across trees, highlighting which recipe attributes are most impactful.

In [25]:
# Initialize the Random Forest model
rf = RandomForestClassifier(random_state=42)

# Set up parameters for Grid Search
param_grid = {
    'n_estimators': [50, 100, 150],  # Number of trees
    'max_depth': [None, 10, 20, 30],  # Depth of each tree
    'min_samples_split': [2, 5, 10],  # Minimum samples required to split an internal node
    'min_samples_leaf': [1, 2, 4],  # Minimum samples required to be at a leaf node
    'max_features': ['auto', 'sqrt', 'log2']  # Number of features to consider for the best split
}

# Perform Grid Search with Cross-Validation
grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=2)
grid_search.fit(X_train, y_train)

# Best parameters from Grid Search
print("Best Parameters:", grid_search.best_params_)

# Train the model with the best parameters
best_rf = grid_search.best_estimator_
best_rf.fit(X_train, y_train)

Fitting 5 folds for each of 324 candidates, totalling 1620 fits
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=20, max_features=log2, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=1, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=5, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=2, min_samples_split=10, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=150; total time=   0.2s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=50; total time=   0.1s
[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   0.1s
Best Parameters: {'max_depth': None, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}
Out[25]:
RandomForestClassifier(max_features='auto', n_estimators=50, random_state=42)
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RandomForestClassifier(max_features='auto', n_estimators=50, random_state=42)
In [27]:
# Predict on the test set
y_pred = best_rf.predict(X_test)

# Detailed Evaluation
print("Accuracy:", accuracy_score(y_test, y_pred))
print("\nClassification Report:\n", classification_report(y_test, y_pred))

# Cross-Validation Score
cv_scores = cross_val_score(best_rf, X, y, cv=5, scoring='accuracy')
print(f"Cross-Validation Accuracy Scores: {cv_scores}")
print(f"Mean Cross-Validation Accuracy: {cv_scores.mean():.2f}")

Accuracy: 1.0

Classification Report:
               precision    recall  f1-score   support

           0       1.00      1.00      1.00       144

    accuracy                           1.00       144
   macro avg       1.00      1.00      1.00       144
weighted avg       1.00      1.00      1.00       144

Cross-Validation Accuracy Scores: [1. 1. 1. 1. 1.]
Mean Cross-Validation Accuracy: 1.00
In [33]:
# Create DataFrame for actual vs. predicted values
results_df = pd.DataFrame({
    'Actual': y_test.reset_index(drop=True),
    'Predicted': y_pred
})

# Calculate percentage of correct predictions (assuming a margin of ±10)
correct_predictions = sum(abs(results_df['Actual'] - results_df['Predicted']) <= 10)
total_predictions = len(results_df)
correct_percentage = (correct_predictions / total_predictions) * 100

# Function to style the DataFrame
def apply_styles():
    """Apply consistent background color with customized styling."""
    styles = {
        'selector': 'th',
        'props': [('background-color', '#000000'), ('color', 'white')]
    }
    return [styles]

# Function to apply the desired background color to all cells
def highlight_background(val):
    """Apply a special color to all cells."""
    return 'background-color: #eac086; color: black'

# Apply the styling to the DataFrame
styled_results_df = results_df.style.applymap(highlight_background)\
                                    .set_table_styles(apply_styles())\
                                    .set_caption(f"Actual vs. Predicted Values (Correct: {correct_percentage:.2f}%)")

# Display the styled DataFrame
styled_results_df
Out[33]:
Actual vs. Predicted Values (Correct: 100.00%)
  Actual Predicted
0 0 0
1 0 0
2 0 0
3 0 0
4 0 0
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Model # 2: Decision Tree Classifier

Reasons for Selection:¶

Clarity and Transparency:¶

Visualizes decisions through a tree structure, making it easy to understand how predictions are made based on recipe features.

Handling Non-Linearity:¶

Captures complex, non-linear relationships between features, useful for modeling intricate interactions affecting recipe traffic.

Feature Insights:¶

Provides direct insights into which features most influence high traffic, helping to identify key attributes driving user engagement.

In [34]:
# Initialize the Decision Tree Classifier
tree_classifier = DecisionTreeClassifier(random_state=42)

# Fit the model
tree_classifier.fit(X_train, y_train)
Out[34]:
DecisionTreeClassifier(random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier(random_state=42)
In [37]:
# Predict on the test set
y_pred_dt = tree_classifier.predict(X_test)

# Detailed Evaluation
print("Accuracy:", accuracy_score(y_test, y_pred_dt))

Accuracy: 1.0
In [38]:
# Create DataFrame for actual vs. predicted values
results_df = pd.DataFrame({
    'Actual': y_test.reset_index(drop=True),
    'Predicted': y_pred_dt
})

# Calculate percentage of correct predictions (assuming a margin of ±10)
correct_predictions = sum(abs(results_df['Actual'] - results_df['Predicted']) <= 10)
total_predictions = len(results_df)
correct_percentage = (correct_predictions / total_predictions) * 100

# Function to style the DataFrame
def apply_styles():
    """Apply consistent background color with customized styling."""
    styles = {
        'selector': 'th',
        'props': [('background-color', '#FFE4E1'), ('color', 'black')]
    }
    return [styles]

# Function to apply the desired background color to all cells
def highlight_background(val):
    """Apply a special color to all cells."""
    return 'background-color: #F0F8FF; color: black'

# Apply the styling to the DataFrame
styled_results_df = results_df.style.applymap(highlight_background)\
                                    .set_table_styles(apply_styles())\
                                    .set_caption(f"Actual vs. Predicted Values (Correct: {correct_percentage:.2f}%)")

# Display the styled DataFrame
styled_results_df
Out[38]:
Actual vs. Predicted Values (Correct: 100.00%)
  Actual Predicted
0 0 0
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
9 0 0
10 0 0
11 0 0
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143 0 0

6 | Models Comparison

🏆 Model Comparison: Why Random Forest Triumphs Over Decision Tree 🏆¶

🎯 Accuracy Boost¶

  • Random Forest: Aggregates predictions from multiple Decision Trees to enhance overall accuracy and minimize overfitting. 🌟
  • Decision Tree: Provides initial insights but can struggle with accuracy and overfitting issues. ❌

🔍 Complex Pattern Detection¶

  • Random Forest: Expertly captures intricate relationships between features by combining diverse tree predictions. 🌐
  • Decision Tree: Limited to simpler patterns and may miss complex interactions. 🚧

🚀 Stability & Robustness¶

  • Random Forest: Delivers greater stability by averaging predictions, reducing the impact of outliers and noise. 🛡️
  • Decision Tree: More prone to instability and overfitting due to reliance on a single tree. ⚠️

🏅 Detailed Feature Importance¶

  • Random Forest: Offers a comprehensive view of feature importance across multiple trees, revealing key drivers of high traffic. 🔍
  • Decision Tree: Provides basic feature importance with less depth and stability. 📉

In a nutshell: Random Forest excels with superior accuracy, complex pattern recognition, and robustness, making it the preferred choice for predicting high traffic! 🌟🚀

7 | Web Page For Prediction

Recipe Predictor Web Application 🍽️¶

Welcome to the Recipe Predictor Web Application! This platform is designed to offer you a seamless experience in discovering recipes based on various inputs. Let’s explore the features and functionality of each page of the application.

📄 Description Page¶

What is Description Page?¶

The Description Page provides an overview of the web application. It introduces the core functionalities and features of the Recipe Predictor, giving users an understanding of what the app can do. This page sets the stage for a smooth user experience by:

  • Explaining the Purpose: Outlines the aim of the application—to predict recipe categories based on user input.
  • Showcasing Key Features: Highlights the main functionalities like recipe prediction and user-friendly design.
  • Providing Technology Insights: Briefly mentions the technologies used, such as Joblib, Flask, and Random Forest.

🔑 Login Page¶

What is Login Page?¶

The Login Page is the entry point for users to access the Recipe Predictor’s features. This page ensures that user data is secure and provides personalized experiences. Key aspects include:

  • User Authentication: Allows users to log in with their credentials, ensuring that their data and preferences are kept private.
  • Registration Option: Provides a way for new users to sign up and create an account.
  • Password Management: Includes options for users to recover or reset their passwords if needed.

🔮 Prediction Page¶

What is Prediction Page?¶

The Prediction Page is where the magic happens! Users can input their recipe details and receive predictions about the best recipe categories based on their inputs. Features of this page include:

  • Input Form: Users can enter details such as recipe name, calories, carbohydrate content, sugar level, protein amount, category, and servings.
  • Real-Time Prediction: After submission, the page communicates with the Random Forest model to generate and display predictions quickly.
  • Interactive Results: Shows predicted categories in an easy-to-understand format, helping users make informed decisions about their recipes.

🛠️ Built With¶

  • Joblib: Utilized for efficient serialization and deserialization of the Random Forest model, ensuring fast and reliable predictions.
  • Flask: Serves as the web framework, handling server requests and API interactions seamlessly.
  • Random Forest: The machine learning model used to classify recipes based on input features.

📄 Description Page¶

🔑 Login Page¶

🔮 Prediction Page¶

🎥 Prediction of Recipe¶

✅ When you have finished...¶

  • Publish your Workspace using the option on the left
  • Check the published version of your report:
    • Can you see everything you want us to grade?
    • Are all the graphics visible?
  • Review the grading rubric. Have you included everything that will be graded?
  • Head back to the Certification Dashboard to submit your practical exam report and record your presentation